Triple

T14075564
Position Surface form Disambiguated ID Type / Status
Subject R2: Doctoral Universities – High research activity E338723 entity
Predicate hasAbbreviation P43 FINISHED
Object R2
R2 is a classification for U.S. doctoral universities characterized by high levels of research activity, as defined by the Carnegie Classification system.
 E1077099 NE FINISHED

How this triple was built (4 steps)

Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.

NER Named-entity recognition gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: R2 | Statement: [R2: Doctoral Universities – High research activity, hasAbbreviation, R2]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: R2
Context triple: [R2: Doctoral Universities – High research activity, hasAbbreviation, R2]
  • A. R2
    R2 is the MBTA station code used to identify Ashmont station on Boston's Red Line transit system.
  • B. R2 Sud
    R2 Sud is a suburban commuter rail line in Catalonia, Spain, that connects Barcelona with southern coastal towns and cities.
  • C. R2 Nord
    R2 Nord is a commuter rail service line that operates in the northern sector of its regional rail network, connecting suburban areas with major urban centers.
  • D. R2N
    R2N is the symbol used to designate the R2 Nord commuter rail line in the Barcelona suburban railway network.
  • E. R2000
    The R2000 is a 32-bit MIPS RISC microprocessor that became one of the earliest and most influential commercial implementations of the MIPS architecture in the mid-1980s.
  • F. None of above. chosen
  • G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg Description generation gpt-5.1
Instruction
Generate a one-sentence description of the target entity. 
You are given a context triple in the form (subject, predicate, object), where the object is the target entity. 
# Instructions
Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. 
Avoid repeating the information from the triple, unless really essential.
# Response Format
Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: R2
Triple: [R2: Doctoral Universities – High research activity, hasAbbreviation, R2]
Generated description
R2 is a classification for U.S. doctoral universities characterized by high levels of research activity, as defined by the Carnegie Classification system.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: R2
Target entity description: R2 is a classification for U.S. doctoral universities characterized by high levels of research activity, as defined by the Carnegie Classification system.
  • A. R2
    R2 is the MBTA station code used to identify Ashmont station on Boston's Red Line transit system.
  • B. R2 Sud
    R2 Sud is a suburban commuter rail line in Catalonia, Spain, that connects Barcelona with southern coastal towns and cities.
  • C. R2 Nord
    R2 Nord is a commuter rail service line that operates in the northern sector of its regional rail network, connecting suburban areas with major urban centers.
  • D. R2N
    R2N is the symbol used to designate the R2 Nord commuter rail line in the Barcelona suburban railway network.
  • E. R2000
    The R2000 is a 32-bit MIPS RISC microprocessor that became one of the earliest and most influential commercial implementations of the MIPS architecture in the mid-1980s.
  • F. None of above. chosen

Provenance (5 batches)

The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.

Step Stage Batch ID Status When
creating Elicitation batch_69d81c687b0c819087fd9ed4198403f8 completed April 9, 2026, 9:38 p.m.
NER Named-entity recognition batch_69de5c5cdd288190914e1d57321b3554 completed April 14, 2026, 3:25 p.m.
NED1 Entity disambiguation (via context triple) batch_69fcb670f51c819088e8d0137f8d3bb1 completed May 7, 2026, 3:57 p.m.
NEDg Description generation batch_69fcbef464248190881012d92777557c completed May 7, 2026, 4:33 p.m.
NED2 Entity disambiguation (via description) batch_69fcbfbae7608190a8d8cf0fa270df9d completed May 7, 2026, 4:37 p.m.
Created at: April 9, 2026, 10:21 p.m.